This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

The curriculum reform in 2022 puts forward new requirements for the professional literacy cultivation of primary science teachers, and the cultivation of primary science classroom teaching skills is an important aspect of the professional literacy cultivation of science education teachers, mainly including subject knowledge and teaching theory, teaching design and preparation, teaching methods and strategies. On the basis of following the principle of combining theory and practice, diversified teaching and student subjectivity, the training strategies of group cooperative learning, observing the teaching process of excellent teachers, and strengthening the effect of micro-grid teaching are proposed, and in addition to the expected evaluation, it provides a certain theoretical basis for the cultivation of normal students in science education.

Science, technology, engineering, and mathematics (STEM) education is a global priority, but effective implementation faces challenges. This bibliometric study analyzed the results of Indonesian STEM education research to elucidate publication and contributor patterns. The Scopus database was searched for Indonesian STEM education publications from 2019–2023 and produced 52 documents from 23 sources. The analysis found a negative average growth rate of −5.43%, with a peak of 14 releases in 2020, possibly related to the COVID-19 pandemic. Although the output was relatively limited, the diversity of sources suggests wide-ranging interest. The leading authors were identified based on their productivity and impact on citation, with Wahono. emerging as the most influential worldwide. Universitas Pendidikan Indonesia was an institutional leader. The Journal of Physics Conference series dominated the contributions and emphasized the role of conference proceedings. Examination of the citations and text frequencies revealed key themes that include technology, engineering, pedagogy, and skills of the 21st century. Several widely cited works ensured international visibility. In general, this bibliometric analysis quantitatively mapped the landscape of Indonesian STEM education research, finding a decline in performance but a strong foundation of committed institutions and authors. The sustainability of production and impact requires targeted policies based on insight into existing strengths, productive scholars, and influential publications. The results provide an empirical basis for practices and policies for the effective development of STEM education in Indonesian schools.